Lead Data Engineer
Summary
Lead a data engineering team to build and maintain Python-based ELT pipelines into BigQuery, design SQLMesh models, and set engineering standards for a modern data stack.
Responsibilities
- Build and evolve the ingestion platform: Python/dltHub pipelines loading into BigQuery, integrating Salesforce, Stripe, Postgres, PostHog, cloud billing, and other GTM systems.
- Build the transformation layer: SQLMesh models across our layered architecture, clean and well‑tested dimensional models, and clear conventions for grain, naming, and audit.
- Improve reliability: expand data quality and observability, build freshness checks, reconciliation tests, and execution monitoring.
- Own the platform infrastructure: BigQuery and supporting GCP, plus Terraform, IAM, service accounts, scheduled jobs, etc.
- Enable the business: deliver trusted datasets to Looker Studio, Google Sheets, and our internal CRM.
- Set technical direction: define engineering standards and architecture, review pipeline and model changes, and mentor engineers and analysts.
Requirements
- 6+ years building and operating production data platforms
- Expert SQL and strong Python
- Experience designing incremental, idempotent, well‑tested pipelines
- Deep experience with BigQuery or another modern cloud data warehouse
- Experience with modern ELT tooling such as SQLMesh, dbt, dltHub, Fivetran, or Airbyte
- Experience with orchestration and CI/CD (GitHub Actions, Airflow, or equivalent)
- Infrastructure‑as‑code experience with Terraform
- Strong data modeling skills: dimensional modeling, warehouse design, testing, and observability
- A track record of technical leadership through architecture, code reviews, and mentoring
Core Competencies
Demonstrates expertise in building and operating production data platforms, with a strong focus on SQL, Python, and modern ELT tooling. Capable of leading technical direction, mentoring teams, and ensuring data quality and observability across cloud data infrastructures.